TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer

Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in diagnosis and treatment, the heterogeneity of BRCA presents significant challenges for effective management and prognosis. Recent studies emphasise the critical role of the tumour microenvironment, particularly immune cells, in influencing tumour behaviour and patient outcomes. This study uses machine learning-based methodologies to investigate the role of tumour protein D52 (TPD52) as a pivotal immune regulator in BRCA. We employed single-cell RNA sequencing (scRNA-seq) to characterise the immune landscape of breast tumours and identify differentially expressed genes (DEGs) associated with TPD52. Our findings indicate that TPD52 may modulate immune cell infiltration and the tumour immune landscape, impacting tumour aggression and patient survival. Furthermore, we performed in vitro validation to elucidate the functional implications of TPD52. By integrating computational analysis with experimental validation, this research highlights TPD52's potential as a biomarker for therapeutic intervention and provides insights into its role in immune regulation within the BRCA microenvironment.